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Abstract #3405

Prostate Cancer Localization Using Multi-Parametric MRI and a Maximum Likelihood Classification Algorithm

Sharon Clarke1, Bruce Daniel2, Jesse McKenney3, Manojkumar Saranathan2, Brian Andrew Hargreaves2, James Brooks4, Harachan Gill4, Mark Gonzalgo4, Benjamin Chung4, Emine U. Saritas5, Ajit Shankaranarayanan6, Graham Sommer2

1Diagnostic Radiology, Dalhousie University, Halifax, Nova Scotia, Canada; 2Diagnostic Radiology, Stanford University, Stanford, CA, United States; 3Anatomic Pathology, Cleveland Clinic, Cleveland, OH, United States; 4Urology, Stanford University, Stanford, CA, United States; 5Bioengineering, University of California Berkeley, Berkeley, CA, United States; 6GE Healthcare, Menlo Park, CA, United States

Segmentation of multi-parametric MR images of the prostate gland using a maximum likelihood classification algorithm correlate well with histopathology. These results show promise for identification of clinically relevant prostate cancer for either MR-guided biopsy or focal therapy.